Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors.

Deep learning is the mainstream paradigm in computer vision and machine learning, but performance is usually not as good as expected when used for applications in robot vision. The problem is that robot sensing is inherently active, and often, relevant data is scarce for many application domains. This calls for novel deep learning approaches that can offer a good performance at a lower data consumption cost. We address here monocular depth estimation in warehouse automation with new methods and three different deep architectures. Our results suggest that the incorporation of sensor models and prior knowledge relative to robotic active vision, can consistently improve the results and learning performance from fewer than usual training samples, as compared to standard data-driven deep learning.

Distance estimation is a challenge for robots, human beings and other animals in their adaptation to changing environments. Different approaches have been proposed to tackle this problem based on classical vision algorithms or, more recently, deep learning. We present a novel approach inspired by mechanisms involved in fixational movements to estimate a depth image with a monocular camera. An algorithm based on microsaccades and head movements during visual fixation is presented. It combines the images generated by these micro-movements with the ego-motion signal, to compute the depth map. Systematic experiments using the Baxter robot in the Gazebo/ROS simulator are described to test the approach in two different scenarios, and evaluate the influence of its parameters and its robustness in the presence of noise.

Saccades are fast eye movements that allow humans and robots to bring the visual target in the center of the visual field. Saccades are open loop with respect to the vision system, thus their execution require a precise knowledge of the internal model of the oculomotor system. In this work, we modeled the saccade control, taking inspiration from the recurrent loops between the cerebellum and the brainstem. In this model, the brainstem acts as a fixed-inverse model of the oculomotor system, while the cerebellum acts as an adaptive element that learns the internal model of the oculomotor system. The adaptive filter is implemented using a state-of-the-art neural network, called I-SSGPR. The proposed approach, namely recurrent architecture, was validated through experiments performed both in simulation and on an antropomorphic robotic head. Moreover, we compared the recurrent architecture with another model of the cerebellum, the feedback error learning. Achieved results show that the recurrent architecture outperforms the feedback error learning in terms of accuracy and insensitivity to the choice of the feedback controller.

This paper presents the alternatives available to support threadprivate data in OpenMP and evaluates them. We show how current compilation systems rely on custom techniques for implementing thread-local data. But in fact the ELF binary specification currently supports data sections that become threadprivate by default. ELF naming for such areas is thread-local storage (TLS). Our experiments demonstrate that implementing threadprivate based on the TLS support is very easy, and more efficient. This proposal goes in the same line as the future implementation of OpenMP on the GNU compiler collection. In addition, our experience with the use of threadprivate in OpenMP applications shows that usually it is better to avoid it. This is because threadprivate variables reside in common blocks and they impede the compiler to fully optimize the code. So it is better to keep threadprivate as a temporary technique only to ease porting MPI codes to OpenMP